Logistics Technology Course

Lesson 1 • Core Logistics Functions

Covers transportation, warehousing, inventory, and order management as distinct functions. Connects each function to overall supply chain performance.

Lesson 2 • Supply Chain Structure and Flows

Defines supply chain tiers, material flows, and information flows. Provides the conceptual map needed for all subsequent technology topics.

Lesson 3 • Logistics Stakeholders and Roles

Identifies shippers, carriers, 3PLs, brokers, and customers and their interdependencies. Prepares students to understand system users in later technology modules.

Lesson 4 • Regulatory and Compliance Fundamentals

Introduces customs, trade compliance, safety standards, and documentation requirements. Grounds students in constraints that logistics technology must address.

Lesson 1 • Technology Categories in Logistics

Surveys TMS, WMS, ERP, visibility platforms, and automation tools. Positions each category relative to the supply chain functions covered in Chapter 1.

Lesson 2 • Technology Adoption Drivers

Analyses cost pressure, customer expectations, and regulatory demands as adoption drivers. Links business context to technology investment decisions.

Lesson 3 • Evaluating Logistics Technology Vendors

Introduces vendor evaluation criteria including functionality, scalability, and support. Equips students to assess tools critically in later applied chapters.

Lesson 1 • Shipment Execution and Tracking

Covers tender management, dispatch, and real-time shipment tracking workflows. Demonstrates how execution data feeds visibility and performance reporting.

Lesson 2 • Freight Audit and Payment

Explains invoice matching, dispute resolution, and payment processing within a TMS. Links audit accuracy to cost control goals established in Chapter 1.

Lesson 3 • Carrier Onboarding and Rate Management

Explains carrier setup, contract rate loading, and spot rate procurement. Prepares students to manage the carrier network within a TMS environment.

Lesson 4 • TMS Architecture and Core Modules

Describes TMS components including order management, load planning, and carrier management. Establishes the system structure before operational workflows are introduced.

Lesson 1 • WMS Architecture and Setup

Covers facility modelling, zone configuration, and user role setup in a WMS. Establishes the system foundation before operational processes are introduced.

Lesson 2 • Inventory Control and Slotting

Explains cycle counting, slotting optimisation, and inventory adjustment processes. Directly supports inventory accuracy metrics introduced in Chapter 1.

Lesson 3 • Order Picking and Fulfilment

Covers pick strategy selection, wave planning, and packing workflows. Demonstrates how WMS directs labour to meet order fulfilment cycle targets.

Lesson 4 • Inbound and Receiving Operations

Details purchase order receipt, quality inspection, and putaway direction workflows. Connects inbound accuracy to downstream inventory reliability.

Lesson 1 • Milestone and Event Management

Defines shipment milestones, expected vs. actual event comparison, and alert configuration. Enables proactive exception management across the supply chain.

Lesson 2 • Carrier and IoT Data Sources

Covers carrier API feeds, GPS telematics, and IoT sensor data as visibility inputs. Explains how diverse data sources are unified into a single tracking view.

Lesson 3 • Visibility Platform Architecture

Explains data ingestion, normalisation, and event-based tracking models in visibility tools. Builds on TMS and WMS data concepts from Chapters 3 and 4.

Lesson 4 • Customer-Facing Visibility Portals

Covers portal design, self-service tracking, and branded communication for end customers. Links visibility capability to customer service outcomes from Chapter 1.

Lesson 1 • Inventory Optimisation Parameters

Explains safety stock calculation, reorder point logic, and service level targets. Directly applies cost and service trade-off concepts from Chapter 1.

Lesson 2 • Replenishment Planning Workflows

Details automated replenishment signals, purchase order generation, and supplier collaboration. Connects planning outputs to procurement and supplier management processes.

Lesson 3 • Planning System Configuration and Governance

Explains master data requirements, parameter governance, and system tuning for planning tools. Ensures students can maintain planning system accuracy over time.

Lesson 4 • Demand Forecasting Methods and Tools

Covers statistical forecasting models, machine learning enhancements, and forecast accuracy metrics. Establishes the demand signal foundation for inventory optimisation.

Lesson 1 • Robotic Picking and Palletising

Covers robotic arm picking, vision systems, and automated palletising applications. Explains how robotics extends WMS-directed labour into physical automation.

Lesson 2 • Automation Implementation and Change Management

Details phased rollout planning, operator training, and performance ramp-up management. Connects technical deployment to workforce and operational readiness.

Lesson 3 • Warehouse Control Systems

Explains WCS role in directing automated equipment and interfacing with WMS. Clarifies the control layer between management software and physical automation.

Lesson 4 • Automation ROI and Feasibility Analysis

Covers throughput modelling, labour displacement analysis, and capital payback calculation. Equips students to build business cases for automation investments.

Lesson 1 • Machine Learning and AI Use Cases

Surveys NLP for document processing, computer vision for inspection, and ML for anomaly detection. Demonstrates AI applications across logistics functions covered in prior chapters.

Lesson 2 • Prescriptive Analytics and Optimisation

Covers network optimisation, dynamic routing, and scenario modelling using prescriptive tools. Advances students from descriptive insight to decision-support outputs.

Lesson 3 • Descriptive and Diagnostic Analytics

Covers dashboard design, KPI benchmarking, and root cause analysis using logistics data. Builds on reporting concepts from TMS, WMS, and visibility chapters.

Lesson 4 • Analytics Governance and Data Strategy

Explains data quality management, model governance, and analytics team structure. Ensures students can sustain analytics programmes beyond initial deployment.